As shown in Fig. REF , our model mainly includes two networks, a student network and a teacher network, and a domain discriminator \(D\) . Each network has a feature extractor \(F\)  and a classifier \(C\) . Given a picture \(x\) , the corresponding feature vector \(f=F\left(x\right)\in \mathbb {R}^d\)  is obtained through the feature extractor \(F\) , where \(d\)  represents the feature dimension, and the corresponding prediction result \(p=C\left(f\right)\in \mathbb {R}^c\)  is obtained through the classifier \(C\) , where \(c\)  represents the total number of classes [1]}. According to [1]}, the adversarial domain adaptation method can be expressed as optimizing the following minimum and maximum problem:
\(\min _{F, C} \max _{D} \mathcal {L}_{c}(F, C)-\lambda _{d} \mathcal {L}_{d}(F, D),\) 
\(\mathcal {L}_{c}(F, C)=-\mathbb {E}_{\left(x_{s}, y_{s}\right) \sim \left(X_{s}, Y_{s}\right)} \sum _{n=1}^{N_{s}}\left[\mathbf {1}_{\left[n=y_{s}\right]} \log \left[C\left(F\left(x_{s}\right)\right)\right]\right],\) 
\(\mathcal {L}_{d}(F, D)=-\mathbb {E}_{x_{s} \sim P_{s}} \log \left[D\left(f_{s}\right)\right]-\mathbb {E}_{x_{t} \sim Q_{t}} \log \left[1-D\left(f_{t}\right)\right]\odot ,\) 
